{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"classification_images_vgg.ipynb","version":"0.3.2","provenance":[{"file_id":"1XzhorsaQwoUvMJyohuI8wZwoYLsTbSsB","timestamp":1565039062582}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"CcDFB3Bk55Ey","colab_type":"code","outputId":"32b793c9-9cfb-4ebc-c535-b0fead8b6939","executionInfo":{"status":"ok","timestamp":1564940150201,"user_tz":240,"elapsed":169,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":85}},"source":["%pylab inline\n","import numpy as np\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.utils.data.dataloader as dataloader\n","import torch.optim as optim\n","\n","from torch.utils.data import TensorDataset\n","from torch.autograd import Variable\n","from torchvision import transforms\n","from torchvision.datasets import MNIST\n","\n","SEED = 1\n","\n","# CUDA?\n","cuda = torch.cuda.is_available()\n","\n","# For reproducibility\n","torch.manual_seed(SEED)\n","\n","if cuda:\n","    torch.cuda.manual_seed(SEED)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Populating the interactive namespace from numpy and matplotlib\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['test']\n","`%matplotlib` prevents importing * from pylab and numpy\n","  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"WDmAvekAs6S-","colab_type":"code","outputId":"7cb116cd-66ec-44ed-d757-7ae96d70d49b","executionInfo":{"status":"ok","timestamp":1564940182780,"user_tz":240,"elapsed":31417,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/drive/')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gLHCEEBp5-wH","colab_type":"code","outputId":"f2793cdf-9c45-469b-84c4-30b8f8303786","executionInfo":{"status":"ok","timestamp":1564940099813,"user_tz":240,"elapsed":4740,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":275}},"source":["train = MNIST('./data', train=True, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","test = MNIST('./data', train=False, download=True, transform=transforms.Compose([\n","    transforms.ToTensor(), # ToTensor does min-max normalization. \n","]), )\n","\n","# Create DataLoader\n","dataloader_args = dict(shuffle=True, batch_size=256,num_workers=4, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n","train_loader = dataloader.DataLoader(train, **dataloader_args)\n","test_loader = dataloader.DataLoader(test, **dataloader_args)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["  0%|          | 0/9912422 [00:00<?, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["9920512it [00:00, 21338563.06it/s]                            \n"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["\r0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["32768it [00:03, 10246.38it/s]            \n","  1%|          | 16384/1648877 [00:00<00:13, 117340.02it/s]"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"],"name":"stdout"},{"output_type":"stream","text":["1654784it [00:00, 5362843.51it/s]                           \n","8192it [00:00, 131892.61it/s]\n"],"name":"stderr"},{"output_type":"stream","text":["Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n","Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n","Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n","Processing...\n","Done!\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"l8P08HhX6GHT","colab_type":"code","colab":{}},"source":["class Model(nn.Module):\n","    def __init__(self):\n","        super(Model, self).__init__()\n","        self.conv1 = nn.Conv2d(1, 20, 5, 1)\n","        self.conv2 = nn.Conv2d(20, 50, 5, 1)\n","        self.fc1 = nn.Linear(4*4*50, 500)\n","        self.fc2 = nn.Linear(500, 10)\n","\n","    def forward(self, x):\n","        x = F.relu(self.conv1(x))\n","        x = F.max_pool2d(x, 2, 2)\n","        x = F.relu(self.conv2(x))\n","        x = F.max_pool2d(x, 2, 2)\n","        x = x.view(-1, 4*4*50)\n","        x = F.relu(self.fc1(x))\n","        x = self.fc2(x)\n","        return F.softmax(x, dim=1)\n","    \n","model = Model()\n","if cuda:\n","    model.cuda() # CUDA!\n","optimizer = optim.Adam(model.parameters(), lr=1e-3)    "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"kCTxYRTR6KNS","colab_type":"code","outputId":"46bf20be-ffd4-48a1-f5a1-9f43590b789e","executionInfo":{"status":"ok","timestamp":1564873274396,"user_tz":240,"elapsed":82141,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":340}},"source":["EPOCHS = 15\n","losses = []\n","\n","model.train()\n","for epoch in range(EPOCHS):\n","    for batch_idx, (data, target) in enumerate(train_loader):\n","        # Get Samples\n","        data, target = Variable(data), Variable(target)\n","        \n","        if cuda:\n","            data, target = data.cuda(), target.cuda()\n","        \n","        # Init\n","        optimizer.zero_grad()\n","\n","        # Predict\n","        y_pred = model(data) \n","\n","        # Calculate loss\n","        loss = F.cross_entropy(y_pred, target)\n","        losses.append(loss.cpu().data)\n","#         losses.append(loss.cpu().data[0])        \n","        # Backpropagation\n","        loss.backward()\n","        optimizer.step()\n","        \n","        \n","        # Display\n","        if batch_idx % 100 == 1:\n","            print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n","                epoch+1,\n","                EPOCHS,\n","                batch_idx * len(data), \n","                len(train_loader.dataset),\n","                100. * batch_idx / len(train_loader), \n","                loss.cpu().data), \n","                end='')\n","    # Eval\n","    evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","    evaluate_y = Variable(test_loader.dataset.test_labels)\n","    if cuda:\n","        evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","    model.eval()\n","    output = model(evaluate_x[:,None,...])\n","    pred = output.data.max(1)[1]\n","    d = pred.eq(evaluate_y.data).cpu()\n","    accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","    \n","    print('\\r Train Epoch: {}/{} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\t Test Accuracy: {:.4f}%'.format(\n","        epoch+1,\n","        EPOCHS,\n","        len(train_loader.dataset), \n","        len(train_loader.dataset),\n","        100. * batch_idx / len(train_loader), \n","        loss.cpu().data,\n","        accuracy*100,\n","        end=''))"],"execution_count":0,"outputs":[{"output_type":"stream","text":[" Train Epoch: 1/15 [60000/60000 (100%)]\tLoss: 1.486982\t Test Accuracy: 95.6200%\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"},{"output_type":"stream","text":[" Train Epoch: 2/15 [60000/60000 (100%)]\tLoss: 1.485699\t Test Accuracy: 97.7800%\n"," Train Epoch: 3/15 [60000/60000 (100%)]\tLoss: 1.461360\t Test Accuracy: 98.0800%\n"," Train Epoch: 4/15 [60000/60000 (100%)]\tLoss: 1.476150\t Test Accuracy: 98.1600%\n"," Train Epoch: 5/15 [60000/60000 (100%)]\tLoss: 1.477142\t Test Accuracy: 98.5100%\n"," Train Epoch: 6/15 [60000/60000 (100%)]\tLoss: 1.474731\t Test Accuracy: 98.5400%\n"," Train Epoch: 7/15 [60000/60000 (100%)]\tLoss: 1.491382\t Test Accuracy: 98.8000%\n"," Train Epoch: 8/15 [60000/60000 (100%)]\tLoss: 1.463181\t Test Accuracy: 98.9900%\n"," Train Epoch: 9/15 [60000/60000 (100%)]\tLoss: 1.466951\t Test Accuracy: 98.7200%\n"," Train Epoch: 10/15 [60000/60000 (100%)]\tLoss: 1.461195\t Test Accuracy: 99.0100%\n"," Train Epoch: 11/15 [60000/60000 (100%)]\tLoss: 1.471871\t Test Accuracy: 98.8700%\n"," Train Epoch: 12/15 [60000/60000 (100%)]\tLoss: 1.461221\t Test Accuracy: 98.8100%\n"," Train Epoch: 13/15 [60000/60000 (100%)]\tLoss: 1.487345\t Test Accuracy: 99.0300%\n"," Train Epoch: 14/15 [60000/60000 (100%)]\tLoss: 1.472627\t Test Accuracy: 98.9700%\n"," Train Epoch: 15/15 [60000/60000 (100%)]\tLoss: 1.461174\t Test Accuracy: 99.1000%\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"35n9_KqYfGX5","colab_type":"code","colab":{}},"source":["# torch.save(model,os.path.join(save_path, 'cnn.pth'))\n","# torch.save(model.state_dict(), os.path.join(save_path, 'cnn_state.pth'))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wIcKVDJJt6VM","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7ind_L9ifSmr","colab_type":"code","outputId":"2caf0861-09ab-4f68-da10-0e92bb26cbc0","executionInfo":{"status":"ok","timestamp":1564869515055,"user_tz":240,"elapsed":211,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["save_path"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'drive/My Drive/classification_images'"]},"metadata":{"tags":[]},"execution_count":154}]},{"cell_type":"code","metadata":{"id":"uhz_O9SP6N2C","colab_type":"code","outputId":"9b4170fe-9748-4f4f-fad9-70bbce11cc8c","executionInfo":{"status":"ok","timestamp":1564873311325,"user_tz":240,"elapsed":280,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","evaluate_y = Variable(test_loader.dataset.test_labels)\n","if cuda:\n","    evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","model.eval()\n","output = model(evaluate_x[:,None,...])\n","pred = output.data.max(1)[1]\n","d = pred.eq(evaluate_y.data).cpu()\n","accuracy = d.sum().type(dtype=torch.float64)/d.size()[0]\n","\n","print('Accuracy:', accuracy*100)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Accuracy: tensor(99.1000, dtype=torch.float64)\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"UpG_rs_Jt8MC","colab_type":"code","outputId":"bb9411d9-235c-45b0-ff1a-39c53a84592b","executionInfo":{"status":"error","timestamp":1564883151006,"user_tz":240,"elapsed":286,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":198}},"source":["import sklearn\n","from sklearn import metrics\n","sklearn.metrics.confusion_matrix(pred.cpu().numpy(), evaluate_y.cpu().numpy())"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-2-a222b4494c4e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_y\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'pred' is not defined"]}]},{"cell_type":"code","metadata":{"id":"rREJUjpnTecf","colab_type":"code","colab":{}},"source":["# uploading the GAN trained model\n","\n","save_path = 'drive/My Drive/classification_images'\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"90lwqE6bkAQz","colab_type":"code","outputId":"4dab825a-1797-4313-abce-d210a2f7b839","executionInfo":{"status":"ok","timestamp":1564954640555,"user_tz":240,"elapsed":344,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":[" z = torch.rand(10, 1, 28, 28) #.cuda()\n"," z[z<.5] = 0\n"," z[z>=.5] = 1\n","  \n"," plt.imshow(z[1].reshape(28,28), cmap = 'gray')"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fc2c7dcde80>"]},"metadata":{"tags":[]},"execution_count":257},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"OTcUTGTVjW2X","colab_type":"code","outputId":"0b9fe563-9172-43ad-85f6-77d2e8a6884e","executionInfo":{"status":"ok","timestamp":1564955246539,"user_tz":240,"elapsed":442973,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# black and white patterns\n","\n","batch_size = 10000\n","all_size = 100000\n","\n","stats = dict()\n","for i in range(10):\n","    stats[i] = 0\n","    \n","    \n","avgs = torch.zeros(100, 10, 28*28)\n","\n","for kk in range(100):\n","  print(kk)\n","  \n","#   z = torch.rand(all_size, 1, 28, 28)*2 -1 #.cuda()\n","  z = torch.rand(all_size, 1, 28, 28) #.cuda()\n","#   z[z<.5] = 0\n","#   z[z<=.5] = 1\n","  \n","  z.cuda()\n","  #plt.imshow(z[1].reshape(28,28))\n","\n","\n","  all_preds = []\n","  all_idx = torch.ones(all_size, dtype = torch.uint8)\n","  for k in range(0,all_size, batch_size):\n","      y_pred = model(z[k:k+batch_size].cuda())\n","      #y_pred[y_pred < 0]= 0\n","\n","      indices = torch.ones(y_pred.size(0), dtype = torch.uint8)\n","      indices[torch.mean(y_pred, dim =1)==0] = 0 \n","\n","      all_idx[k:k+batch_size] = indices \n","      pred = y_pred[indices==1].data.max(1)[1]\n","\n","      all_preds.append(pred)\n","\n","  pred = torch.cat(all_preds)\n","\n","  for i in range(10):\n","    stats[i] += torch.sum(pred==i)\n","  \n","  z = z[all_idx]\n","  for i in range(10):\n","      a = torch.mean(z[pred==i] , dim=0) \n","      avgs[kk, i] = a.reshape(28*28)\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0\n","1\n","2\n","3\n","4\n","5\n","6\n","7\n","8\n","9\n","10\n","11\n","12\n","13\n","14\n","15\n","16\n","17\n","18\n","19\n","20\n","21\n","22\n","23\n","24\n","25\n","26\n","27\n","28\n","29\n","30\n","31\n","32\n","33\n","34\n","35\n","36\n","37\n","38\n","39\n","40\n","41\n","42\n","43\n","44\n","45\n","46\n","47\n","48\n","49\n","50\n","51\n","52\n","53\n","54\n","55\n","56\n","57\n","58\n","59\n","60\n","61\n","62\n","63\n","64\n","65\n","66\n","67\n","68\n","69\n","70\n","71\n","72\n","73\n","74\n","75\n","76\n","77\n","78\n","79\n","80\n","81\n","82\n","83\n","84\n","85\n","86\n","87\n","88\n","89\n","90\n","91\n","92\n","93\n","94\n","95\n","96\n","97\n","98\n","99\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"FTXQe7ko7ZQV","colab_type":"code","outputId":"260423a7-f319-4aba-e8e1-8dc39d932d62","executionInfo":{"status":"ok","timestamp":1564873683713,"user_tz":240,"elapsed":119392,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["batch_size = 10000\n","all_size = 100000\n","\n","stats = dict()\n","for i in range(10):\n","    stats[i] = 0\n","    \n","    \n","avgs = torch.zeros(100, 10, 28*28)\n","\n","for kk in range(100):\n","  print(kk)\n","  \n","#   z = torch.rand(all_size, 1, 28, 28)*2 -1 #.cuda()\n","  z = torch.rand(all_size, 1, 28, 28) #.cuda()\n","  \n","  z.cuda()\n","  #plt.imshow(z[1].reshape(28,28))\n","\n","\n","  all_preds = []\n","  all_idx = torch.ones(all_size, dtype = torch.uint8)\n","  for k in range(0,all_size, batch_size):\n","      y_pred = model(z[k:k+batch_size].cuda())\n","      #y_pred[y_pred < 0]= 0\n","\n","      indices = torch.ones(y_pred.size(0), dtype = torch.uint8)\n","      indices[torch.mean(y_pred, dim =1)==0] = 0 \n","\n","      all_idx[k:k+batch_size] = indices \n","      pred = y_pred[indices==1].data.max(1)[1]\n","\n","      all_preds.append(pred)\n","\n","  pred = torch.cat(all_preds)\n","\n","  for i in range(10):\n","    stats[i] += torch.sum(pred==i)\n","  \n","  z = z[all_idx]\n","  for i in range(10):\n","      a = torch.mean(z[pred==i] , dim=0) \n","      avgs[kk, i] = a.reshape(28*28)\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0\n","1\n","2\n","3\n","4\n","5\n","6\n","7\n","8\n","9\n","10\n","11\n","12\n","13\n","14\n","15\n","16\n","17\n","18\n","19\n","20\n","21\n","22\n","23\n","24\n","25\n","26\n","27\n","28\n","29\n","30\n","31\n","32\n","33\n","34\n","35\n","36\n","37\n","38\n","39\n","40\n","41\n","42\n","43\n","44\n","45\n","46\n","47\n","48\n","49\n","50\n","51\n","52\n","53\n","54\n","55\n","56\n","57\n","58\n","59\n","60\n","61\n","62\n","63\n","64\n","65\n","66\n","67\n","68\n","69\n","70\n","71\n","72\n","73\n","74\n","75\n","76\n","77\n","78\n","79\n","80\n","81\n","82\n","83\n","84\n","85\n","86\n","87\n","88\n","89\n","90\n","91\n","92\n","93\n","94\n","95\n","96\n","97\n","98\n","99\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"J5Z4VfN0QpBL","colab_type":"code","colab":{}},"source":["z = torch.rand(1000000, 784)\n","grand_mean = torch.mean(z,dim=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"tNu7VTILZxE1","colab_type":"code","colab":{}},"source":["# torch.nn.functional.softmax(a)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"P_qVlUIwZwf9","colab_type":"code","outputId":"4a4059ab-efd2-407a-f1f7-af53854e1ae2","executionInfo":{"status":"ok","timestamp":1564873472315,"user_tz":240,"elapsed":303,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["stats"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0: tensor(8477, device='cuda:0'),\n"," 1: tensor(315, device='cuda:0'),\n"," 2: tensor(114741, device='cuda:0'),\n"," 3: tensor(36349, device='cuda:0'),\n"," 4: tensor(37842, device='cuda:0'),\n"," 5: tensor(12396, device='cuda:0'),\n"," 6: tensor(731, device='cuda:0'),\n"," 7: tensor(3253, device='cuda:0'),\n"," 8: tensor(782547, device='cuda:0'),\n"," 9: tensor(3349, device='cuda:0')}"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"dvHCYz6xRALW","colab_type":"code","outputId":"5822fbd3-1267-4a02-b69d-e258154d41ab","executionInfo":{"status":"ok","timestamp":1564865785774,"user_tz":240,"elapsed":213,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["grand_mean.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([784])"]},"metadata":{"tags":[]},"execution_count":69}]},{"cell_type":"code","metadata":{"id":"PLTo_R17UUvZ","colab_type":"code","outputId":"3cdd81db-4288-4105-dae1-d6e08607f939","executionInfo":{"status":"ok","timestamp":1564866669275,"user_tz":240,"elapsed":305,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["b.data.max(1)[1]\n","b\n","a.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([28, 28])"]},"metadata":{"tags":[]},"execution_count":77}]},{"cell_type":"code","metadata":{"id":"WaJqeovzgriQ","colab_type":"code","colab":{}},"source":["for i in range(10):\n","    print(torch.sum(pred==i))  \n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"A6DDnDB784s3","colab_type":"code","outputId":"cefaaf66-23ef-45e2-b5a0-3d580dbab656","executionInfo":{"status":"ok","timestamp":1564955408331,"user_tz":240,"elapsed":1612,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["save_path = 'drive/My Drive/classification_images'\n","import os\n","dd = torch.mean(avgs, dim=0)\n","#dd = dd - grand_mean\n","for kk in range(10):\n","  \n","  fig = plt.figure()\n","  a = dd[kk]\n","  a = a.view(-1,28)\n","  b = model(a[None,None,...].cuda())\n","\n","  #a = torch.nn.functional.log_softmax(a)# torch.nn.functional.softmax(a)\n","  c = b.data.max(1)[1]\n","  plt.title(f'gt: {str(kk)}  -  pred: {str(c.cpu().data[0].numpy())} -  size-gt: {stats[kk]}')  \n","  plt.imshow(a) #, cmap = 'gray')\n","  #fig.savefig(os.path.join(save_path, str(kk)+'-.png'))\n","  "],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAEICAYAAACQ6CLfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS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iuqVJyBoXSq48RYHBIbHE4PNx2l3ALUM+GysfH+0N7blw4F1oP0TIDaujisVJnIlBXv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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:395: UserWarning: Warning: converting a masked element to nan.\n","  dv = (np.float64(self.norm.vmax) -\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:396: UserWarning: Warning: converting a masked element to nan.\n","  np.float64(self.norm.vmin))\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:403: UserWarning: Warning: converting a masked element to nan.\n","  a_min = np.float64(newmin)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:408: UserWarning: Warning: converting a masked element to nan.\n","  a_max = np.float64(newmax)\n","/usr/local/lib/python3.6/dist-packages/matplotlib/colors.py:918: UserWarning: Warning: converting a masked element to nan.\n","  dtype = np.min_scalar_type(value)\n","/usr/local/lib/python3.6/dist-packages/numpy/ma/core.py:713: UserWarning: Warning: converting a masked element to nan.\n","  data = np.array(a, copy=False, subok=subok)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAEICAYAAACQ6CLfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3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size 432x288 with 1 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size 432x288 with 1 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+czz3jSeVsTdksC/b5ffhUj2nPdNou1FLGbluy2e226uqww6jHh+3U3PC4IZeX3OHI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\nCZR/A/sjNiSHI8yuAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"CB-oRv2nrGdy","colab_type":"code","colab":{}},"source":["#   \n","# - load the model\n","# - modulate the input\n","# - see how many are classified as the pattern\n","\n","import os\n","\n","model = Model()\n","if cuda:\n","    model.cuda() # CUDA!\n","    \n","model = torch.load(os.path.join(save_path, 'cnn.pth'))\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"44QqDqOZ_Zzp","colab_type":"code","colab":{}},"source":["import torch.nn.functional as F\n","def resize2d(img, size):\n","    return (F.adaptive_avg_pool2d(Variable(img,volatile=True), size)).data\n","\n","  \n","from skimage import io, transform  \n","import sklearn\n","from sklearn import metrics\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZglFZOUL1oB4","colab_type":"code","outputId":"f6ace11d-0900-4d42-dbd6-a967abb59de9","executionInfo":{"status":"ok","timestamp":1564952687379,"user_tz":240,"elapsed":5850,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# load the zero image\n","objects = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9')\n","\n","z = torch.rand(100, 1, 28, 28) #.cuda()\n","\n","for i in range(10):\n","\n","  pattern =  io.imread(os.path.join(save_path, str(i) + '-.png'))\n","  pattern = pattern[35:35+217,113:330,0]\n","  pattern = transform.resize(pattern, (28, 28))\n","  # pattern = torch.from_numpy(pattern)\n","\n","  pattern = torch.from_numpy(pattern)\n","  pattern = pattern.type(torch.FloatTensor)\n","\n","  plt.figure()\n","  plt.subplot(151)\n","  plt.imshow(pattern)\n","\n","\n","  plt.subplot(152)\n","  plt.imshow(torch.squeeze(z[0,...],0))\n","\n","  y_pred = model(z.cuda())\n","  print(y_pred.data.max(1)[1])\n","  \n","  \n","  plt.subplot(153)\n","  freq = y_pred.data.max(1)[1].cpu()\n","  aa = numpy.histogram(freq.numpy()) \n","  y_pos = np.arange(len(aa[0]))\n","  plt.bar(y_pos, aa[0]/aa[0].max())\n","  plt.xticks(y_pos, objects)\n","\n","  \n","  z_new = z + (.9*pattern)\n","\n","  plt.subplot(154)\n","  plt.imshow(torch.squeeze(z_new[0,...],0))\n","\n","  y_pred = model(z_new.cuda())\n","#   print(y_pred.data.max(1)[1])\n","  \n","  plt.subplot(155)\n","  freq = y_pred.data.max(1)[1].cpu()\n","  aa = numpy.histogram(freq.numpy()) \n","  y_pos = np.arange(len(aa[0]))\n","  plt.bar(y_pos, aa[0]/aa[0].max())\n","  plt.xticks(y_pos, objects)\n","  print(y_pred.data.max(1)[1])\n","#   plt.bar()\n","#   plt.hist(y_pred.data.max(1)[1].cpu())\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n","        8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8,\n","        0, 8, 8, 8, 2, 8, 8, 8, 8, 8, 9, 8, 8, 4, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8,\n","        4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 2, 4, 4, 8,\n","        8, 8, 2, 8], device='cuda:0')\n","tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0, 0, 0, 0,\n","        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0, 0, 0], device='cuda:0')\n","tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n","        8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8,\n","        0, 8, 8, 8, 2, 8, 8, 8, 8, 8, 9, 8, 8, 4, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8,\n","        4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 2, 4, 4, 8,\n","        8, 8, 2, 8], device='cuda:0')\n","tensor([1, 1, 1, 1, 3, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2,\n","        8, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,\n","        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 8, 1,\n","        1, 1, 1, 1, 1, 1, 1, 8, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 2, 1, 1, 1,\n","        8, 1, 1, 1], device='cuda:0')\n","tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n","        8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8,\n","        0, 8, 8, 8, 2, 8, 8, 8, 8, 8, 9, 8, 8, 4, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8,\n","        4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 2, 4, 4, 8,\n","        8, 8, 2, 8], device='cuda:0')\n","tensor([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n","        2, 2, 2, 2], device='cuda:0')\n","tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n","        8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8,\n","        0, 8, 8, 8, 2, 8, 8, 8, 8, 8, 9, 8, 8, 4, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8,\n","        4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 2, 4, 4, 8,\n","        8, 8, 2, 8], device='cuda:0')\n","tensor([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n","        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n","        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n","        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n","        3, 3, 3, 3], device='cuda:0')\n","tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n","        8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8,\n","        0, 8, 8, 8, 2, 8, 8, 8, 8, 8, 9, 8, 8, 4, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8,\n","        4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 2, 4, 4, 8,\n","        8, 8, 2, 8], device='cuda:0')\n","tensor([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n","        4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n","        4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n","        4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n","        4, 4, 4, 4], device='cuda:0')\n","tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n","       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8,\n","        8, 8, 2, 8], device='cuda:0')\n","tensor([6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n","        6, 4, 6, 6, 6, 6, 6, 6, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n","        6, 6, 6, 6, 6, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n","        6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n","        6, 6, 6, 6], device='cuda:0')\n","tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n","        8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8,\n","        0, 8, 8, 8, 2, 8, 8, 8, 8, 8, 9, 8, 8, 4, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8,\n","        4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 2, 4, 4, 8,\n","        8, 8, 2, 8], device='cuda:0')\n","tensor([7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n","        7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n","        7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n","        7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n","        7, 7, 7, 7], device='cuda:0')\n","tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n","        8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8,\n","        0, 8, 8, 8, 2, 8, 8, 8, 8, 8, 9, 8, 8, 4, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8,\n","        4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 2, 4, 4, 8,\n","        8, 8, 2, 8], device='cuda:0')\n","tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n","        8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n","        8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n","        8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n","        8, 8, 8, 8], device='cuda:0')\n","tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n","        8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8,\n","        0, 8, 8, 8, 2, 8, 8, 8, 8, 8, 9, 8, 8, 4, 8, 8, 0, 8, 8, 8, 8, 8, 8, 8,\n","        4, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 2, 4, 4, 8,\n","        8, 8, 2, 8], device='cuda:0')\n","tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 4, 0, 9, 9, 9, 9, 9,\n","        9, 9, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 9, 9, 9, 9, 9, 9, 4, 4, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n","        9, 9, 9, 9, 9, 9, 9, 8, 9, 3, 9, 9, 8, 9, 3, 9, 9, 9, 9, 9, 9, 9, 4, 9,\n","        4, 9, 9, 9], device='cuda:0')\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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3otWNwvWkano7R90oq0Hh9dqn7NghP1u1b00v0XTNDlxaasUGu6660+70p39WU+V3CYr5S2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\n/NAUMkcfTMYoT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Qt26Jz7nng+WSdb3vjnHub+GZ0bejSNe3K/bKj+sTYlF1ZTsBk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\n","text/plain":["<Figure 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ZfvakJ3Bjko8FqtM83DNUCvb/SZyB1TWKkjtK9syN1AfT9mwpv8Z76jIVlueAoPSZj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\n5Jwb45wbk52d3b6NMzoVJivNJ6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BXPyUjxvKQ+OC/+b3MxwysiYWmfXeBtCOy5WS4VK7tq5PXSBqP1K/SopvaKG1De09R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\nkCm+8T+Nkh3a/1UkVt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CeWq7WdPfbfRejp5pyz60f6yul3/3+n2j8+UWPv6LXZ0Yn7Z26VGeqrnlU/e2DvlZz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\nrkJggg==\n","text/plain":["<Figure 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N+1reL3/7tZj0eG6D3+KF//g8Azfkqf/0q9BnM+UDTIi95WOekOk7Q2pq9L37h79VP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\ntl7s/JmPSpmmkTqT//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YJrlLyve67UuLPL338X3J9WmTS3pmrZKbqhkfF3z50pbRrc4YskNByl9Tp47seAiD2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\nSHFtBFLSCNbK4Oa+2C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nx3x2pU+vDVaop3dzgg8G9he6aGmlFkpoK6X3VMs9P12Q+kaxU/9m6DkGMeWNO3FKI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\nrkJggg==\n","text/plain":["<Figure 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vIVcc0KmZJrvYGVqWr9RppUas18M03NSWt0b5FahYHPvTmVvXbBz2HRE+iefObM6Up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\n56f94G0AHks5Ug8Yq5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RT6IZhGCHBFLphGEZIMIVuGIYREkyhG4ZhhART6IZhGCHBFLph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\nGCHBFLphGEZI+P+1ZsZzh33ViQAAAABJRU5ErkJggg==\n","text/plain":["<Figure 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LKXKMjh9VH6D16f6Ttnzm7LFlH0dH6oXMrtQHvXarxyatP1lWntX30/HnjXwNg4kK1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\n","text/plain":["<Figure size 432x288 with 5 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"gm0-JahuWkDC","colab_type":"code","colab":{}},"source":["digits = next(iter(train_loader))\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"94JurDSAW0EW","colab_type":"code","colab":{}},"source":["from torchvision import transforms, datasets\n","def mnist_data():\n","    compose = transforms.Compose(\n","        [transforms.ToTensor(),\n","         transforms.Normalize((.5), (.5))         \n","#          transforms.Normalize((.5, .5, .5), (.5, .5, .5))\n","        ])\n","    transform = transforms.Compose([\n","        transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])    \n","    \n","    out_dir = './dataset'\n","    return datasets.MNIST(root=out_dir, train=True, transform=transform, download=True)\n","\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"H3lSioNEYxqu","colab_type":"code","outputId":"3df31623-bb3c-473c-a52f-37080f4d83cf","executionInfo":{"status":"error","timestamp":1564951730148,"user_tz":240,"elapsed":203,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":164}},"source":["train_loader"],"execution_count":0,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-222-b60042ec47b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mAttributeError\u001b[0m: 'DataLoader' object has no attribute 'data'"]}]},{"cell_type":"code","metadata":{"id":"vq-33xNK0dSw","colab_type":"code","outputId":"cc57de87-93f0-4d1b-a802-0b6ea5b446fd","executionInfo":{"status":"ok","timestamp":1564952889960,"user_tz":240,"elapsed":5556,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# ls 'drive/My Drive/classification_images'\n","# torch.squeeze(z[0,...],0).shape\n","# plt.hist(y_pred.data.max(1)[1].cpu())\n","\n","objects = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9')\n","\n","# evaluate_x = Variable(test_loader.dataset.test_data.type_as(torch.FloatTensor()))\n","# evaluate_y = Variable(test_loader.dataset.test_labels)\n","# if cuda:\n","#     evaluate_x, evaluate_y = evaluate_x.cuda(), evaluate_y.cuda()\n","\n","\n","#digits = next(iter(train_loader))\n","\n","\n","\n","# Load data\n","data = mnist_data()\n","\n","tt = data.targets[data.targets==9]\n","dd = data.data[data.targets==9] \n","data.targets = tt\n","data.data = dd\n","data_loader = torch.utils.data.DataLoader(data, batch_size=100, shuffle=True, drop_last =True)\n","# Num batches\n","\n","z = next(iter(data_loader)) \n","\n","\n","for i in range(10):\n","\n","  pattern =  io.imread(os.path.join(save_path, str(i) + '-.png'))\n","  pattern = pattern[35:35+217,113:330,0]\n","  pattern = transform.resize(pattern, (28, 28))\n","  # pattern = torch.from_numpy(pattern)\n","\n","  pattern = torch.from_numpy(pattern)\n","  pattern = pattern.type(torch.FloatTensor)\n","\n","  plt.figure()\n","  plt.subplot(151)\n","  plt.imshow(pattern)\n","\n","  z_old = (z[0] + 1)/2\n","  \n","  plt.subplot(152)\n","  plt.imshow(torch.squeeze(z_old[0,...],0))\n","\n","  y_pred = model(z_old.cuda())\n","  print(y_pred.data.max(1)[1])\n","  \n","  \n","  plt.subplot(153)\n","  freq = y_pred.data.max(1)[1].cpu()\n","  aa = numpy.histogram(freq.numpy()) \n","  y_pos = np.arange(len(aa[0]))\n","  plt.bar(y_pos, aa[0]/aa[0].max())\n","  plt.xticks(y_pos, objects)\n","\n","  \n","  z_new = .3*z_old + pattern\n","\n","  plt.subplot(154)\n","  plt.imshow(torch.squeeze(z_new[0,...],0))\n","\n","  y_pred = model(z_new.cuda())\n","#   print(y_pred.data.max(1)[1])\n","  \n","  plt.subplot(155)\n","  freq = y_pred.data.max(1)[1].cpu()\n","  aa = numpy.histogram(freq.numpy()) \n","  y_pos = np.arange(len(aa[0]))\n","  plt.bar(y_pos, aa[0]/aa[0].max())\n","  plt.xticks(y_pos, objects)\n","  print(y_pred.data.max(1)[1])\n","#   plt.bar()\n","#   plt.hist(y_pred.data.max(1)[1].cpu())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 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device='cuda:0')\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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5mtiPtXAHtmpopzEy+V2Pe3n2beFZfQue5iAJoWdFE8FDpbT8VnIsZndHSU6667jte97nW8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\nVuAnwG8BlwGP1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size 432x288 with 5 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size 432x288 with 5 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DPUgorlsIU5HPdFFetorJnhPGtj3PWO/8dR7/4lZN7J/NEfiCMI9ywftu0558th97K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\nd78HuKfl2G81fH5t2y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8BZGuLulzoc2VWFrqZtRKU+Wfd/Stx9y4zWxuPrwWeXZgmLk28WuHJm29gxWnns/z0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\nzH5iZvfE/f+t7pySmd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bFpcdImljfPF1rnBABsLE/ZLht7wsWRnkonmdBXWTNgjT/Ypq1PF6zOMb22z/J4tde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\n","text/plain":["<Figure size 432x288 with 5 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"sAP_1AyrIJQp","colab_type":"code","outputId":"e0fdbdc1-af5a-49c1-9a31-9eccad9781d4","executionInfo":{"status":"ok","timestamp":1564951868791,"user_tz":240,"elapsed":197,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["  z.min()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(0.)"]},"metadata":{"tags":[]},"execution_count":232}]},{"cell_type":"code","metadata":{"id":"REXMt5SvgdbK","colab_type":"code","outputId":"cf279773-9d32-4b57-dc0a-3c8f0d207efc","executionInfo":{"status":"ok","timestamp":1564869780908,"user_tz":240,"elapsed":313,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["for i in range(10):\n","    print(torch.sum(pred==i))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tensor(236, device='cuda:0')\n","tensor(3, device='cuda:0')\n","tensor(1874, device='cuda:0')\n","tensor(160, device='cuda:0')\n","tensor(2689, device='cuda:0')\n","tensor(22, device='cuda:0')\n","tensor(44, device='cuda:0')\n","tensor(81, device='cuda:0')\n","tensor(93796, device='cuda:0')\n","tensor(98, device='cuda:0')\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DQa2gDav9kKr","colab_type":"code","outputId":"db3940f6-bccd-4b68-e5d1-990a3e0776e4","executionInfo":{"status":"error","timestamp":1564867442930,"user_tz":240,"elapsed":288,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":300}},"source":["# ls\n","from google.colab import files\n","files.download('./*.png')\n"],"execution_count":0,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-96-9eb9ebc38cb1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./*.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mdownload\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    142\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=undefined-variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m   \u001b[0mstarted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_threading\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEvent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: Cannot find file: ./*.png"]}]},{"cell_type":"code","metadata":{"id":"SBxJ8w3AYAbz","colab_type":"code","colab":{}},"source":["ls drive/My\\ Drive/classification_images\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3GAnOj4gXjd8","colab_type":"code","outputId":"8b431de1-784f-4821-caca-18c31191ec3f","executionInfo":{"status":"ok","timestamp":1564867529154,"user_tz":240,"elapsed":24357,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":[""],"execution_count":0,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"x1TYqjT17ejE","colab_type":"code","outputId":"ce93940e-d31e-48b7-cdee-26778485fc75","executionInfo":{"status":"ok","timestamp":1564860495533,"user_tz":240,"elapsed":2235,"user":{"displayName":"Ali Borji","photoUrl":"https://lh4.googleusercontent.com/-zK8jl944MFs/AAAAAAAAAAI/AAAAAAAAAKo/JE-YlX3KgWk/s64/photo.jpg","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# z = z[all_idx]\n","pred = torch.cat(all_preds)\n","for i in range(10):\n","    plt.figure()\n","    a = torch.mean(z[pred==i] , dim=0) \n","    a = a.view(-1,28)\n","    b = model(a[None,None,...].cuda())\n","    c = b.data.max(1)[1]\n","    plt.title(f'gt: {str(i)}  -  pred: {str(c.cpu().data[0].numpy())} -  size-gt: {z[pred==i].size(0)}')\n","    plt.imshow(1-a)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAEICAYAAACQ6CLfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJztnXt4VOW59u8n55AECInkwPnkARBB\nEVDEgvWsLWJbqnV7afUr7a5u2+rXfn7d7VW/ax/qtlut2t3ujUrV1rZbW92iVSuiApYNEpGjHMUA\nCSEcEiAJIYeZ5/tjVtxDzPvMEJKZkff+Xddcmcy93rXetWbdM2vW8z7PK6oKQoh/pCW7A4SQ5EDz\nE+IpND8hnkLzE+IpND8hnkLzE+IpNL9niIiKyOhk98NCRDaKyMxk9+NUxyvzi8hMEak6wTYiIv8i\nIgeDx7+IiPRWH1MJEckWkQUickRE9orI3YnYrqqOU9V3ErGtDkSkUkQuPYHls0Tkj0E7dX1YBctt\nOtHzLhF4Zf5uMg/AdQDOATABwBcAfDOpPQoQkfRe3sR9AMYAGAZgFoAfiMiVvbzNzxLvAvgbAHuN\nZb4PYH9iunOCqOop9QBwLoAPADQAeB7AfwL4RwB5AJoBhAE0Bo/yONa3HMC8qP9vB7Cil/o+E0AV\ngB8COACgEsBNUfpTAH4F4FUATQAuBZAN4F8B7AJQC+DfAeRGtfk+gBoAewDcBkABjI6zP3sAXB71\n/z8A+EMP7WsxgFcAHAJQB2AZgLRAqwRwafD8UNT71RT0f3igXQtgTbDMcgATjO3lAngaQD2ATQB+\nAKAq0H4TnBfNwXZ+cIL7UgVgZhevjwi2dVXHtlLpkfQO9OjOAFkAdgL4DoBMANcDaAXwj4E+s/Ob\nAOAiAIeMdR4GMDXq/8kAGnqp/zMBtAN4KDD154IT/oxAfyroz3RErtpyADwMYCGAAQAKALwM4KfB\n8lcGHwjjEfnw+120+QF8DcA6R18Kg2VLol77MoD1PbSvPw0+qDKDxwwAEmifmL9Tm38GsDRYfhKA\nfQCmAkgHcEvQLtuxvfsBLAn2azCAddHnQlfbDJb5Whz74jL/KwDmdHXepcLjVLvsnwYgA8Cjqtqm\nqi8AeM9qoKrvqmp/Y5F8RAzXwWEA+b38u//HqtqiqksA/BnA3CjtJVX9q6qGAbQg8rPke6pap6oN\niBjkhmDZuQB+raobVLUJkcv4T1DV36nqBEcf8oO/nfe94GR2LIo2AGUAhgXv1TINHNMVIvJVRD6s\nvqSqbYjs93+o6kpVDanq04gcj2mOVcwF8M+qWq+qVQAejdVBVZ2gqr87wf3q6O8cAOmq+mJ32ieC\nU8385QCqO51Eu09ynY0A+kb93xdAo3WidhDctW4MHjPi3F59YNQOdiKyXx1E789pAPoAeF9EDonI\nIQCvB68jaBe9/M44+wBE9hv49L43dLVwN/b1ZwC2A3hDRHaIyL2uBUVkEoBfAJijqh2/n4cBuKdj\nv4N9HwKgXERuiurLa8HynY/FyZ4XTkQkD8ADAO7qrW30BBnJ7kAPUwNgkIhIlDmHAPgoeN6dFMaN\niNzs67iCOCd4LSaqOq4b2ysUkbyoD4ChADZErzbq+QFEfqeOU9XqLtZVg8j+dzA03k6oar2I1CCy\nv4uCl537fqL7Glyl3IOIgccDeEtEVqnq4ujlRGQggP8CcIeqfhAl7QbwT6r6T45NPNvp/xpELvc/\nDP4f0knvyfTWMQCGA1gWXCBmAegnInsBTFPVyh7cVrc51b75/xtACMCdIpIhIrMBTInSawEUiUi/\nE1jnMwDuFpFBIlKOyAn7VE912MH/C0JEMxC5qfV8VwsFl/6PA3g4MAmCfl4RLPIcgFtFZKyI9AHw\nkxPsxzMAfiQihSJyJoBvoIf2XUSuFZHRwc+nw4i8b+FOy2QA+COA36rqc51W8TiAb4nI1CAcmyci\n14iI62fJcwD+b7AvgwDc2UmvBTDyBPchW0Rygn+zRCQn2J8NiHy4TAwe/ytY/0T04hXHCZPsmw49\n/UDkhtwaRC5bnwfwAiK/oTv0BQAOInKHuByRG02NxvoEkUu4uuDxAIIbU73Q95mI3Dz6e0S+1XcB\nuDlKfwrBzcuo13IQ+Z2/A8ARRO4u3xWl34tIKOpTd/sB3ARgo9Gf7OB4HUHk5L27B/f1e4jcZGsK\n9jn6PapEJJIxPOhvE/7njn8jgKHBclcCWBW8lzXB+13g2F4eInf1DwXH6EcAPorSZwfH+xCA/x28\nthFR0ZYu1lkZ9C/6Mdz1vibbG50fHXdXT1lEZCWAf1fVXye7L7EIBor8VlUHJ7svpzoi8rcAblDV\nzyW7L8niVLvsh4h8TkRKg8v+WxAZmPN6svtFkouIlInIdBFJE5EzEPn5lrJ34hPBqXbDDwDOQOT3\nXR4il8JfVtWa5HaJpABZAP4DkYE3hwD8AcAvk9qjJHPKX/YTQrrmlLvsJ4TER0Iv+9Pz8jSzcIBT\nL+h71Gzf0JLj1NIa7M+xUJ59hZOd1WbqbSF3Dk04ZG87IzNk6uEG+20oLjpi6vsa3YPusrPt/Wpp\nzjL144NvnyY9t91ufrT7p5jkxjhurTHymoy+Dy6sM5vub8039dYG+7il2V1HKNd9Pkp79wePttXX\nIdTUFNcKTsr8QYbXI4iMrX5CVe+3ls8sHIDBd37Pqc+6dI25vSWV7jT0vMX2m1V/Qaupnz7USswC\nqg+7hwY0Hc4125aUHDL1hndKTP22m+37lY+tuMSpnTHSvt2xdaMdWEg/an+w9R170NSPvl/s1MLp\n9gdyxlj7Q+/YbnukcVqL2wP3z+k8Buh45u++2NR3LbHHS2XXmzIOn+3+UM48GMOWxmGr+sXDdtso\nun3ZH6ST/hsiGUtjAdwoImO7uz5CSGI5md/8UwBsV9UdqtqKyN3T2T3TLUJIb3My5h+E44cqVgWv\nHYeIzBORChGpCDU1dZYJIUmi1+/2q+p8VZ2sqpPT8/J6e3OEkDg5GfNX4/jMqMHBa4SQzwAnY/5V\nAMaIyAgRyUKkgMTCnukWIaS36XaoT1XbReROAH9BJNS3QFXNPPe8gmZMm+VeJDvNjhlPH/qxU1tR\n5CpIE+Ha8etM/bWtdjq67nGPMcgd0WV9i0+o/cgd7gKAnExTxmPv2kVlC0rd29+yo8xeeYxwm8SI\n8x/ZVGS3z3avf+KMrWbbD5afbur5e+xw9pGx7nDawZAdGt66yw6/njbVrsl5oNI9ngWww3ntBfZB\nzy1rdGqSE2OAQRQnFedX1VcRKSZJCPmMweG9hHgKzU+Ip9D8hHgKzU+Ip9D8hHgKzU+IpyQ0n7+x\nMRfLl7sT/3JixMvL+rlTPM/7wganBgCvLZ5s6hkj3bFTAMgZ407LLc63cxY+asw29bSqGHnpMWLx\nR7e5JxzKHGzXSJhwhj0o8/2NdjXrGedsNvU1tZ9K9/iEVTHWnb/fjuNf//V3TD0nzR3nf3TTLLNt\nRq2dr1+/3x67kT642dRhpIEXj7BrDdSvN9Kkj8U/dyu/+QnxFJqfEE+h+QnxFJqfEE+h+QnxFJqf\nEE9JaKgvrR3I2e/+vJFae/Lc7cPdlYA+anOHlABg8ES7Ou+edaWmfvUlK53aW1V26mn/QjsUmDHD\nDnGmvXWaqeddWuvUJhTtMdsueW2Sqd963Tum/tTyi0z9kkkfOrWV4WFm21CRHep7+t0Zpp5W2OLW\ndtkVlzXTDq+GY+jlA+zKw3uNFPE0sdc9/Pwqp7Y/z65Sfdx24l6SEHJKQfMT4ik0PyGeQvMT4ik0\nPyGeQvMT4ik0PyGektA4P/JCkKnu1NiiPDv9tHWVuwx1+9BjZtuq2kJTzxpmp/S+8upUp3bOLLsE\n9ar1o0w9VsruNTe8b+qT8nc6tZ+uvspsO+0ydxweALY02iWsy0ccMPW333eXRF/xxYfMtjNXftPU\nB46wZwiub+jj1Aon2KW3m1rslN4+L9hjUvYW2fp3rnrNqT22bqbZtrTcPS4k1hiB45aNe0lCyCkF\nzU+Ip9D8hHgKzU+Ip9D8hHgKzU+Ip9D8hHhKQuP84bCg+ai7jPXBv7pLUANAqMw9dbGG7dzvnK3u\n/GkACPWxy2u39XXHT88qsGsFVOTZeeu/vPBZU7/7qdtNfcPF7vEPabvs/V4pw01da+z2WfX290fh\nVPc4gAuX3WG2PXfoblNfW23XcAiF3H0Lhe1+W+cpALTaJRwg1fZxe7T6aqd2xzXuMQAA8Miyy51a\nc7M9PiGakzK/iFQCaAAQAtCuqnZxfEJIytAT3/yzVNUe5kUISTn4m58QTzlZ8yuAN0TkfRGZ19UC\nIjJPRCpEpCLUYNeyI4QkjpO97L9IVatFZCCARSKyWVWXRi+gqvMBzAeA7JGD4s86IIT0Kif1za+q\n1cHffQBeBDClJzpFCOl9um1+EckTkYKO5wAuB2BPlUsISRlO5rK/BMCLItKxnt+p6uvmxjLCOM2o\nZ35shj2tca4Rm81MD5lt6wfZu5rRYE9tPPhMd2383663L3hmnW7n+9/1x9tM/dLZq039ja1nObXQ\ngHazbdYOu3791M9vNPXVL4039aPH3PHykhi17SvWjjb1omH1pt7+qnsq62OX2fXtQ832+RAe6p4T\nAAAyqu1xAiOmuscwPLboSrNtVpm77oVkxP/LutvmV9UdAM7pbntCSHJhqI8QT6H5CfEUmp8QT6H5\nCfEUmp8QT0loSq+q4FhrplM/0mCHnXLXu/U2O9IHjLZDXrE4+I47bVYG2OGVt9vP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PiKTQ/IZ5C8xPiKTQ/IZ4SNW+/iOQDeBhALgAFsERV7xGROwB8A8Ce8KO3qeor1rLq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\nzU+Ip9D8hHjK/wcNT6gLuPN+1gAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"vs1QSHq79WhR","colab_type":"code","colab":{}},"source":["# for i in range(z.size(0)):\n","#   print(torch.max(data[1,0]))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"5d6-r9IX8wz2","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}